Many traditional diagnostics and monitoring platforms for electric machines use a two-step process to identify fault conditions. For example, typical diagnostics and monitoring platforms first use physics model-based or other non-learning routines to flag data believed to show a fault condition. And, in a subsequent step, an expert technician performs a more thorough analysis using any combination of additional analytical tools and expert experience to evaluate the data to determine whether the data shows either a properly diagnosed fault condition or a false positive condition, before enunciating the fault condition.
These additional analytical steps add to the time between when a fault occurs and when the fault condition is enunciated to the customer. As such, typical diagnostics and monitoring platforms add complexity to diagnostics and monitoring operations, and they require expert-level technician input in order to properly analyze fault data, both conditions that may cause unwanted down time and loss of revenue.